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Rick WicklinSeptember 25, 2017 6

Simulate multivariate normal data in SAS by using PROC SIMNORMAL

My article about Fisher's transformation of the Pearson correlation contained a simulation. The simulation uses the RANDNORMAL function in SAS/IML software to simulate multivariate normal data. If you are a SAS programmer who does not have access to SAS/IML software, you can use the SIMNORMAL procedure in SAS/STAT software to

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Advanced Analytics | Learn SAS | Programming Tips

Simulate clustered data from a Gaussian mixture distribution

Rick WicklinSeptember 13, 2017 7

Simulate multivariate clusters in SAS

This article shows how to simulate data from a mixture of multivariate normal distributions, which is also called a Gaussian mixture. You can use this simulation to generate clustered data. The adjacent graph shows three clusters, each simulated from a four-dimensional normal distribution. Each cluster has its own within-cluster covariance,

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Analytics | Programming Tips

Rick WicklinSeptember 11, 2017 1

Symbolic derivatives in SAS

Did you know that you can get SAS to compute symbolic (analytical) derivatives of simple functions, including applying the product rule, quotient rule, and chain rule? SAS can form the symbolic derivatives of single-variable functions and partial derivatives of multivariable functions. Furthermore, the derivatives are output in a form that

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Learn SAS | Programming Tips

Rick WicklinSeptember 7, 2017 2

Construct polynomial effects in SAS regression models

If you use SAS regression procedures, you are probably familiar with the "stars and bars" notation, which enables you to construct interaction effects in regression models. Although you can construct many regression models by using that classical notation, a friend recently reminded me that the EFFECT statement in SAS provides

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Programming Tips

Rick WicklinAugust 23, 2017 1

The arithmetic-geometric mean

All statisticians are familiar with the classical arithmetic mean. Some statisticians are also familiar with the geometric mean. Whereas the arithmetic mean of n numbers is the sum divided by n, the geometric mean of n nonnegative numbers is the n_th root of the product of the numbers. The geometric

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Programming Tips

Rick WicklinAugust 21, 2017 1

6 tips for timing the performance of algorithms

When you implement a statistical algorithm in a vector-matrix language such as SAS/IML, R, or MATLAB, you should measure the performance of your implementation, which means that you should time how long a program takes to analyze data of varying sizes and characteristics. There are some general tips that can

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Programming Tips

Rick WicklinAugust 7, 2017 6

The curse of non-unique eigenvectors

A SAS customer asked, "I computed the eigenvectors of a matrix in SAS and in another software package. I got different answers? How do I know which answer is correct?" I've been asked variations of this question dozens of times. The answer is usually "both answers are correct." The mathematical

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Programming Tips

Broken-stick method for retaining principal components

Rick WicklinAugust 2, 2017 9

Dimension reduction: Guidelines for retaining principal components

Last week I blogged about the broken-stick problem in probability, which reminded me that the broken-stick model is one of the many techniques that have been proposed for choosing the number of principal components to retain during a principal component analysis. Recall that for a principal component analysis (PCA) of

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Learn SAS | Programming Tips

Rick WicklinJuly 31, 2017 4

Flip it. Flip it good.

A SAS user needed to convert a program from MATLAB into the SAS/IML matrix language and asked whether there is a SAS/IML equivalent to the fliplr and flipud functions in MATLAB. These functions flip the columns or rows (respectively) of a matrix; "LR" stands for "left-right" and "UD" stands for

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Programming Tips

Broken stick problem: What is the probability that three randomly chosen points will break a segment into a triangle?

Rick WicklinJuly 26, 2017 1

Random segments and broken sticks

A classical problem in elementary probability asks for the expected lengths of line segments that result from randomly selecting k points along a segment of unit length. It is both fun and instructive to simulate such problems. This article uses simulation in the SAS/IML language to estimate solutions to the

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